Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot


mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f8af1bf55c0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f8af1b61978>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
/usr/local/lib/python3.5/dist-packages/ipykernel/__main__.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_input_image = tf.placeholder(tf.float32, shape=(None, image_width,image_height,image_channels),name='real_input_image')
    z_input=tf.placeholder(tf.float32,shape=(None,z_dim),name='z_input')
    learning_rate=tf.placeholder(tf.float32,name='learning_rate')
    return real_input_image, z_input, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    alpha=.02
    # TODO: Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        
        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
    return out, logits
    


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha=.02
    with tf.variable_scope('generator', reuse=not is_train):
        # First fully connected layer
        x1 = tf.layers.dense(z, 2*2*512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 2, 2, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
         
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
        
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
   
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    d_updates = [opt for opt in ops if opt.name.startswith('discriminator')]
    g_updates = [opt for opt in ops if opt.name.startswith('generator')]
    with tf.control_dependencies(d_updates):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
    with tf.control_dependencies(g_updates):
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
        
   
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    _,image_width,image_height,image_channels=data_shape
    
    real_input_image, z_input,lr = model_inputs(image_width, image_height, image_channels, z_dim)
    d_loss, g_loss = model_loss(real_input_image, z_input, image_channels)
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    steps = 0
    interval =10
    losses=[]
    z_size = 100
    n_images = 25
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                batch_images *= 2.0
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={real_input_image: batch_images, z_input: batch_z,lr:learning_rate})
                _ = sess.run(g_opt, feed_dict={z_input: batch_z, lr:learning_rate})
                
                

                if steps % interval == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({z_input: batch_z, real_input_image: batch_images})
                    train_loss_g = g_loss.eval({z_input: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))
                    show_generator_output(sess, n_images, z_input, image_channels, data_image_mode)

  
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 128
z_dim = 100
learning_rate = .0002
beta1 = .5
alpha = 0.2
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.2149... Generator Loss: 2.6751
Epoch 1/2... Discriminator Loss: 0.2739... Generator Loss: 2.4674
Epoch 1/2... Discriminator Loss: 1.1436... Generator Loss: 0.4299
Epoch 1/2... Discriminator Loss: 0.5953... Generator Loss: 0.9068
Epoch 1/2... Discriminator Loss: 0.1207... Generator Loss: 2.2893
Epoch 1/2... Discriminator Loss: 0.2259... Generator Loss: 8.9155
Epoch 1/2... Discriminator Loss: 1.7676... Generator Loss: 0.2055
Epoch 1/2... Discriminator Loss: 1.3042... Generator Loss: 11.8804
Epoch 1/2... Discriminator Loss: 0.0561... Generator Loss: 5.6827
Epoch 1/2... Discriminator Loss: 0.2579... Generator Loss: 1.6191
Epoch 1/2... Discriminator Loss: 0.1387... Generator Loss: 2.4670
Epoch 1/2... Discriminator Loss: 0.6279... Generator Loss: 9.6288
Epoch 1/2... Discriminator Loss: 0.1381... Generator Loss: 7.6108
Epoch 1/2... Discriminator Loss: 0.3165... Generator Loss: 2.4578
Epoch 1/2... Discriminator Loss: 0.2292... Generator Loss: 2.4754
Epoch 1/2... Discriminator Loss: 0.1778... Generator Loss: 2.6868
Epoch 1/2... Discriminator Loss: 0.2654... Generator Loss: 2.1311
Epoch 1/2... Discriminator Loss: 0.1287... Generator Loss: 2.7720
Epoch 1/2... Discriminator Loss: 0.1202... Generator Loss: 3.0865
Epoch 1/2... Discriminator Loss: 0.2045... Generator Loss: 2.4394
Epoch 1/2... Discriminator Loss: 0.1445... Generator Loss: 2.9983
Epoch 1/2... Discriminator Loss: 0.1481... Generator Loss: 2.9046
Epoch 1/2... Discriminator Loss: 0.1952... Generator Loss: 2.2279
Epoch 1/2... Discriminator Loss: 0.0943... Generator Loss: 3.5581
Epoch 1/2... Discriminator Loss: 0.0853... Generator Loss: 4.7715
Epoch 1/2... Discriminator Loss: 0.0912... Generator Loss: 4.6991
Epoch 1/2... Discriminator Loss: 0.2680... Generator Loss: 2.3220
Epoch 1/2... Discriminator Loss: 0.1638... Generator Loss: 2.7280
Epoch 1/2... Discriminator Loss: 0.0871... Generator Loss: 3.4303
Epoch 1/2... Discriminator Loss: 0.2021... Generator Loss: 2.9091
Epoch 1/2... Discriminator Loss: 0.0962... Generator Loss: 3.4903
Epoch 1/2... Discriminator Loss: 0.0726... Generator Loss: 3.6039
Epoch 1/2... Discriminator Loss: 0.0543... Generator Loss: 3.5884
Epoch 1/2... Discriminator Loss: 0.0857... Generator Loss: 3.5271
Epoch 1/2... Discriminator Loss: 0.0674... Generator Loss: 3.5324
Epoch 1/2... Discriminator Loss: 0.1262... Generator Loss: 3.6208
Epoch 1/2... Discriminator Loss: 0.1040... Generator Loss: 3.3476
Epoch 1/2... Discriminator Loss: 0.1341... Generator Loss: 9.3371
Epoch 1/2... Discriminator Loss: 0.1617... Generator Loss: 2.6002
Epoch 1/2... Discriminator Loss: 0.4493... Generator Loss: 1.3611
Epoch 1/2... Discriminator Loss: 0.2852... Generator Loss: 5.1607
Epoch 1/2... Discriminator Loss: 0.1318... Generator Loss: 3.6435
Epoch 1/2... Discriminator Loss: 0.0958... Generator Loss: 3.5521
Epoch 1/2... Discriminator Loss: 0.1276... Generator Loss: 3.3693
Epoch 1/2... Discriminator Loss: 0.1138... Generator Loss: 3.7501
Epoch 1/2... Discriminator Loss: 0.0988... Generator Loss: 6.4336
Epoch 2/2... Discriminator Loss: 0.2793... Generator Loss: 1.9567
Epoch 2/2... Discriminator Loss: 0.0486... Generator Loss: 4.7622
Epoch 2/2... Discriminator Loss: 0.1938... Generator Loss: 2.3261
Epoch 2/2... Discriminator Loss: 0.1358... Generator Loss: 2.5533
Epoch 2/2... Discriminator Loss: 0.1319... Generator Loss: 2.9863
Epoch 2/2... Discriminator Loss: 0.8104... Generator Loss: 0.8262
Epoch 2/2... Discriminator Loss: 0.1913... Generator Loss: 2.8105
Epoch 2/2... Discriminator Loss: 0.1726... Generator Loss: 2.6674
Epoch 2/2... Discriminator Loss: 0.2122... Generator Loss: 2.9771
Epoch 2/2... Discriminator Loss: 0.1006... Generator Loss: 3.3571
Epoch 2/2... Discriminator Loss: 0.6216... Generator Loss: 9.9179
Epoch 2/2... Discriminator Loss: 0.0959... Generator Loss: 3.4166
Epoch 2/2... Discriminator Loss: 0.1629... Generator Loss: 2.8800
Epoch 2/2... Discriminator Loss: 0.1419... Generator Loss: 3.0313
Epoch 2/2... Discriminator Loss: 0.0671... Generator Loss: 4.3083
Epoch 2/2... Discriminator Loss: 0.1255... Generator Loss: 3.1503
Epoch 2/2... Discriminator Loss: 0.1160... Generator Loss: 3.3129
Epoch 2/2... Discriminator Loss: 0.0893... Generator Loss: 3.6507
Epoch 2/2... Discriminator Loss: 0.4577... Generator Loss: 7.0876
Epoch 2/2... Discriminator Loss: 0.2517... Generator Loss: 3.5628
Epoch 2/2... Discriminator Loss: 0.1383... Generator Loss: 3.7553
Epoch 2/2... Discriminator Loss: 0.2493... Generator Loss: 2.1247
Epoch 2/2... Discriminator Loss: 0.1470... Generator Loss: 2.9049
Epoch 2/2... Discriminator Loss: 0.0970... Generator Loss: 4.7296
Epoch 2/2... Discriminator Loss: 0.1479... Generator Loss: 3.0408
Epoch 2/2... Discriminator Loss: 0.1687... Generator Loss: 2.9176
Epoch 2/2... Discriminator Loss: 0.2876... Generator Loss: 2.1574
Epoch 2/2... Discriminator Loss: 0.2199... Generator Loss: 4.3693
Epoch 2/2... Discriminator Loss: 0.1431... Generator Loss: 3.4764
Epoch 2/2... Discriminator Loss: 0.1768... Generator Loss: 2.4030
Epoch 2/2... Discriminator Loss: 0.4730... Generator Loss: 9.6031
Epoch 2/2... Discriminator Loss: 0.2612... Generator Loss: 5.4495
Epoch 2/2... Discriminator Loss: 0.1874... Generator Loss: 3.0933
Epoch 2/2... Discriminator Loss: 0.1654... Generator Loss: 3.8881
Epoch 2/2... Discriminator Loss: 0.0877... Generator Loss: 3.6400
Epoch 2/2... Discriminator Loss: 0.2079... Generator Loss: 2.9683
Epoch 2/2... Discriminator Loss: 0.3130... Generator Loss: 2.3941
Epoch 2/2... Discriminator Loss: 0.7877... Generator Loss: 7.3553
Epoch 2/2... Discriminator Loss: 0.1928... Generator Loss: 3.4700
Epoch 2/2... Discriminator Loss: 0.2101... Generator Loss: 2.9360
Epoch 2/2... Discriminator Loss: 0.3297... Generator Loss: 1.8325
Epoch 2/2... Discriminator Loss: 0.2068... Generator Loss: 2.7339
Epoch 2/2... Discriminator Loss: 0.3046... Generator Loss: 2.4479
Epoch 2/2... Discriminator Loss: 0.2547... Generator Loss: 2.5767
Epoch 2/2... Discriminator Loss: 0.4589... Generator Loss: 1.7081
Epoch 2/2... Discriminator Loss: 0.5005... Generator Loss: 4.9911
Epoch 2/2... Discriminator Loss: 0.1627... Generator Loss: 3.0170

Minst DataSet Result Comparison:

The goal of this project is to improve a balance between

With a limited time I have left for this course and considering the amount of time it takes to generate the image, I have decided to compare the results that are generated in 72 hours.

Of all the hyper paremeter combinations that have generated results so far, there is some convergence of generator and discriminator loss in these two combinations of hyper parameter.

  • 1) batch_size: 16,z_dim: 128,learning_rate_list: 5e-05,beta1: 0.4,alpha: 0.12 Epoch 1/2... Discriminator Loss: 0.9277... Generator Loss: 0.9402
  • 2) batch_size: 16,z_dim: 128,learning_rate_list: 5e-05,beta1: 0.5,alpha: 0.1 Epoch 1/2... Discriminator Loss: 0.5329... Generator Loss: 2.0431

Reference: 1) https://hackernoon.com/how-do-gans-intuitively-work-2dda07f247a1 2)https://openreview.net/forum?id=SyBPtQfAZ

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size = 128
z_dim = 100
learning_rate = .0002
beta1 = .5
alpha = 0.2


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 0.6017... Generator Loss: 1.9618
Epoch 1/1... Discriminator Loss: 0.3129... Generator Loss: 2.3199
Epoch 1/1... Discriminator Loss: 0.2019... Generator Loss: 3.0300
Epoch 1/1... Discriminator Loss: 0.2459... Generator Loss: 2.2441
Epoch 1/1... Discriminator Loss: 0.1810... Generator Loss: 2.6506
Epoch 1/1... Discriminator Loss: 0.5999... Generator Loss: 0.8486
Epoch 1/1... Discriminator Loss: 0.3846... Generator Loss: 10.0873
Epoch 1/1... Discriminator Loss: 0.1859... Generator Loss: 10.0106
Epoch 1/1... Discriminator Loss: 1.8633... Generator Loss: 0.1871
Epoch 1/1... Discriminator Loss: 0.4132... Generator Loss: 2.1667
Epoch 1/1... Discriminator Loss: 0.8264... Generator Loss: 0.6635
Epoch 1/1... Discriminator Loss: 0.1742... Generator Loss: 2.1755
Epoch 1/1... Discriminator Loss: 0.1840... Generator Loss: 2.8874
Epoch 1/1... Discriminator Loss: 0.7625... Generator Loss: 11.4869
Epoch 1/1... Discriminator Loss: 0.1001... Generator Loss: 5.3113
Epoch 1/1... Discriminator Loss: 0.4207... Generator Loss: 1.3930
Epoch 1/1... Discriminator Loss: 0.1086... Generator Loss: 3.0697
Epoch 1/1... Discriminator Loss: 0.3401... Generator Loss: 2.3296
Epoch 1/1... Discriminator Loss: 0.2229... Generator Loss: 2.3529
Epoch 1/1... Discriminator Loss: 0.3326... Generator Loss: 2.0359
Epoch 1/1... Discriminator Loss: 0.7804... Generator Loss: 1.1210
Epoch 1/1... Discriminator Loss: 0.9850... Generator Loss: 2.3766
Epoch 1/1... Discriminator Loss: 0.4943... Generator Loss: 1.3839
Epoch 1/1... Discriminator Loss: 0.4391... Generator Loss: 1.7197
Epoch 1/1... Discriminator Loss: 0.5175... Generator Loss: 1.4014
Epoch 1/1... Discriminator Loss: 0.4230... Generator Loss: 1.4245
Epoch 1/1... Discriminator Loss: 0.5306... Generator Loss: 1.5150
Epoch 1/1... Discriminator Loss: 0.6807... Generator Loss: 4.1426
Epoch 1/1... Discriminator Loss: 0.4319... Generator Loss: 1.4550
Epoch 1/1... Discriminator Loss: 0.8565... Generator Loss: 4.0021
Epoch 1/1... Discriminator Loss: 0.3339... Generator Loss: 2.3186
Epoch 1/1... Discriminator Loss: 0.5626... Generator Loss: 1.8651
Epoch 1/1... Discriminator Loss: 0.4150... Generator Loss: 2.5014
Epoch 1/1... Discriminator Loss: 0.3939... Generator Loss: 1.7860
Epoch 1/1... Discriminator Loss: 0.4143... Generator Loss: 2.1261
Epoch 1/1... Discriminator Loss: 0.2641... Generator Loss: 3.6227
Epoch 1/1... Discriminator Loss: 0.2707... Generator Loss: 2.3450
Epoch 1/1... Discriminator Loss: 0.4606... Generator Loss: 1.6170
Epoch 1/1... Discriminator Loss: 0.1640... Generator Loss: 2.9225
Epoch 1/1... Discriminator Loss: 0.5342... Generator Loss: 1.4556
Epoch 1/1... Discriminator Loss: 0.5061... Generator Loss: 1.4317
Epoch 1/1... Discriminator Loss: 0.3175... Generator Loss: 1.8924
Epoch 1/1... Discriminator Loss: 0.4658... Generator Loss: 1.8983
Epoch 1/1... Discriminator Loss: 0.5330... Generator Loss: 1.8146
Epoch 1/1... Discriminator Loss: 0.6472... Generator Loss: 1.2936
Epoch 1/1... Discriminator Loss: 0.5692... Generator Loss: 1.2732
Epoch 1/1... Discriminator Loss: 0.2413... Generator Loss: 2.4571
Epoch 1/1... Discriminator Loss: 0.6458... Generator Loss: 1.5875
Epoch 1/1... Discriminator Loss: 0.3767... Generator Loss: 2.4418
Epoch 1/1... Discriminator Loss: 0.7335... Generator Loss: 3.2580
Epoch 1/1... Discriminator Loss: 0.3416... Generator Loss: 3.5150
Epoch 1/1... Discriminator Loss: 0.3291... Generator Loss: 2.1283
Epoch 1/1... Discriminator Loss: 0.8063... Generator Loss: 4.5624
Epoch 1/1... Discriminator Loss: 0.2846... Generator Loss: 2.2530
Epoch 1/1... Discriminator Loss: 0.4342... Generator Loss: 1.5799
Epoch 1/1... Discriminator Loss: 1.5409... Generator Loss: 0.3982
Epoch 1/1... Discriminator Loss: 0.5512... Generator Loss: 1.4074
Epoch 1/1... Discriminator Loss: 0.6032... Generator Loss: 1.4284
Epoch 1/1... Discriminator Loss: 0.4069... Generator Loss: 2.5753
Epoch 1/1... Discriminator Loss: 0.5417... Generator Loss: 2.9532
Epoch 1/1... Discriminator Loss: 0.4292... Generator Loss: 1.6295
Epoch 1/1... Discriminator Loss: 0.4039... Generator Loss: 3.6709
Epoch 1/1... Discriminator Loss: 0.7611... Generator Loss: 1.2234
Epoch 1/1... Discriminator Loss: 0.7242... Generator Loss: 1.5699
Epoch 1/1... Discriminator Loss: 0.6322... Generator Loss: 1.3208
Epoch 1/1... Discriminator Loss: 0.3709... Generator Loss: 2.0731
Epoch 1/1... Discriminator Loss: 0.4233... Generator Loss: 1.7499
Epoch 1/1... Discriminator Loss: 0.3625... Generator Loss: 1.9826
Epoch 1/1... Discriminator Loss: 0.7919... Generator Loss: 0.8872
Epoch 1/1... Discriminator Loss: 0.9092... Generator Loss: 0.8436
Epoch 1/1... Discriminator Loss: 0.5483... Generator Loss: 1.5741
Epoch 1/1... Discriminator Loss: 0.6093... Generator Loss: 3.1410
Epoch 1/1... Discriminator Loss: 0.4673... Generator Loss: 2.0591
Epoch 1/1... Discriminator Loss: 0.3240... Generator Loss: 2.2623
Epoch 1/1... Discriminator Loss: 0.7997... Generator Loss: 1.3310
Epoch 1/1... Discriminator Loss: 0.5631... Generator Loss: 1.4619
Epoch 1/1... Discriminator Loss: 0.6291... Generator Loss: 1.4547
Epoch 1/1... Discriminator Loss: 0.5444... Generator Loss: 1.8432
Epoch 1/1... Discriminator Loss: 1.4255... Generator Loss: 0.4709
Epoch 1/1... Discriminator Loss: 0.7019... Generator Loss: 1.8621
Epoch 1/1... Discriminator Loss: 0.5058... Generator Loss: 1.8923
Epoch 1/1... Discriminator Loss: 0.5350... Generator Loss: 2.7884
Epoch 1/1... Discriminator Loss: 0.6159... Generator Loss: 1.4420
Epoch 1/1... Discriminator Loss: 0.5656... Generator Loss: 2.6595
Epoch 1/1... Discriminator Loss: 0.4379... Generator Loss: 1.9763
Epoch 1/1... Discriminator Loss: 0.4094... Generator Loss: 1.9645
Epoch 1/1... Discriminator Loss: 0.3502... Generator Loss: 2.0867
Epoch 1/1... Discriminator Loss: 0.4593... Generator Loss: 1.6898
Epoch 1/1... Discriminator Loss: 1.2218... Generator Loss: 0.5226
Epoch 1/1... Discriminator Loss: 1.0865... Generator Loss: 3.8009
Epoch 1/1... Discriminator Loss: 0.7124... Generator Loss: 2.7437
Epoch 1/1... Discriminator Loss: 0.5450... Generator Loss: 2.5726
Epoch 1/1... Discriminator Loss: 0.5936... Generator Loss: 2.7690
Epoch 1/1... Discriminator Loss: 0.4751... Generator Loss: 1.5846
Epoch 1/1... Discriminator Loss: 0.3295... Generator Loss: 2.3565
Epoch 1/1... Discriminator Loss: 0.8859... Generator Loss: 3.2402
Epoch 1/1... Discriminator Loss: 0.5042... Generator Loss: 1.9807
Epoch 1/1... Discriminator Loss: 0.5433... Generator Loss: 1.5102
Epoch 1/1... Discriminator Loss: 0.5278... Generator Loss: 1.5603
Epoch 1/1... Discriminator Loss: 0.8791... Generator Loss: 0.8820
Epoch 1/1... Discriminator Loss: 0.7913... Generator Loss: 4.1524
Epoch 1/1... Discriminator Loss: 0.3326... Generator Loss: 3.0673
Epoch 1/1... Discriminator Loss: 0.3672... Generator Loss: 1.9691
Epoch 1/1... Discriminator Loss: 3.7525... Generator Loss: 8.2025
Epoch 1/1... Discriminator Loss: 0.3560... Generator Loss: 2.1477
Epoch 1/1... Discriminator Loss: 0.4349... Generator Loss: 2.1182
Epoch 1/1... Discriminator Loss: 0.6640... Generator Loss: 3.1545
Epoch 1/1... Discriminator Loss: 0.5197... Generator Loss: 4.2243
Epoch 1/1... Discriminator Loss: 0.3283... Generator Loss: 3.2357
Epoch 1/1... Discriminator Loss: 0.3196... Generator Loss: 2.5027
Epoch 1/1... Discriminator Loss: 0.3789... Generator Loss: 2.0892
Epoch 1/1... Discriminator Loss: 4.3011... Generator Loss: 7.9384
Epoch 1/1... Discriminator Loss: 0.7004... Generator Loss: 1.6839
Epoch 1/1... Discriminator Loss: 0.6605... Generator Loss: 1.3006
Epoch 1/1... Discriminator Loss: 0.4279... Generator Loss: 1.9911
Epoch 1/1... Discriminator Loss: 0.4990... Generator Loss: 3.2162
Epoch 1/1... Discriminator Loss: 1.0986... Generator Loss: 0.5877
Epoch 1/1... Discriminator Loss: 1.2671... Generator Loss: 4.3047
Epoch 1/1... Discriminator Loss: 0.4173... Generator Loss: 2.2761
Epoch 1/1... Discriminator Loss: 0.5537... Generator Loss: 2.7187
Epoch 1/1... Discriminator Loss: 0.4831... Generator Loss: 1.8513
Epoch 1/1... Discriminator Loss: 1.0463... Generator Loss: 0.6827
Epoch 1/1... Discriminator Loss: 0.8993... Generator Loss: 2.4255
Epoch 1/1... Discriminator Loss: 0.4979... Generator Loss: 1.7163
Epoch 1/1... Discriminator Loss: 0.3599... Generator Loss: 2.2203
Epoch 1/1... Discriminator Loss: 0.6021... Generator Loss: 1.2797
Epoch 1/1... Discriminator Loss: 0.5336... Generator Loss: 1.4848
Epoch 1/1... Discriminator Loss: 0.4304... Generator Loss: 1.7902
Epoch 1/1... Discriminator Loss: 0.9980... Generator Loss: 0.9311
Epoch 1/1... Discriminator Loss: 0.6253... Generator Loss: 1.2539
Epoch 1/1... Discriminator Loss: 0.5876... Generator Loss: 3.7372
Epoch 1/1... Discriminator Loss: 1.2312... Generator Loss: 0.5459
Epoch 1/1... Discriminator Loss: 0.6757... Generator Loss: 1.3329
Epoch 1/1... Discriminator Loss: 0.3812... Generator Loss: 2.4065
Epoch 1/1... Discriminator Loss: 0.5019... Generator Loss: 3.3538
Epoch 1/1... Discriminator Loss: 0.7976... Generator Loss: 0.9073
Epoch 1/1... Discriminator Loss: 0.5941... Generator Loss: 1.6949
Epoch 1/1... Discriminator Loss: 0.7118... Generator Loss: 1.0831
Epoch 1/1... Discriminator Loss: 0.3839... Generator Loss: 2.5936
Epoch 1/1... Discriminator Loss: 0.2973... Generator Loss: 2.4287
Epoch 1/1... Discriminator Loss: 0.9417... Generator Loss: 0.7757
Epoch 1/1... Discriminator Loss: 0.7140... Generator Loss: 1.1529
Epoch 1/1... Discriminator Loss: 0.6647... Generator Loss: 3.1765
Epoch 1/1... Discriminator Loss: 0.3508... Generator Loss: 2.3697
Epoch 1/1... Discriminator Loss: 0.8093... Generator Loss: 4.7501
Epoch 1/1... Discriminator Loss: 0.8342... Generator Loss: 1.3915
Epoch 1/1... Discriminator Loss: 1.1320... Generator Loss: 0.5761
Epoch 1/1... Discriminator Loss: 0.6546... Generator Loss: 2.8429
Epoch 1/1... Discriminator Loss: 0.6802... Generator Loss: 1.1795
Epoch 1/1... Discriminator Loss: 0.4228... Generator Loss: 2.6076
Epoch 1/1... Discriminator Loss: 0.2868... Generator Loss: 2.2273
Epoch 1/1... Discriminator Loss: 0.8429... Generator Loss: 4.1226
Epoch 1/1... Discriminator Loss: 0.3623... Generator Loss: 2.0189
Epoch 1/1... Discriminator Loss: 1.1492... Generator Loss: 3.9955
Epoch 1/1... Discriminator Loss: 0.6538... Generator Loss: 1.2586
Epoch 1/1... Discriminator Loss: 0.8446... Generator Loss: 4.2771
Epoch 1/1... Discriminator Loss: 0.9032... Generator Loss: 3.2662
Epoch 1/1... Discriminator Loss: 0.5991... Generator Loss: 1.2500

Celeba DataSet Result Comparison:

With a limited time I have left for this course and considering the amount of time it takes to generate the image, I have decided to compare the results that are generated in 48 hours.

The differences in results were more prominent in case of black and white images. With only a few rounds , it is very hard to really distinguish between the images visually.So I looked for a balance in Generator Loss and Discriminator Loss. Of all the hyper paremeter combinations that have generated results so far, there is some convergence of generator and discriminator loss in these two combinations of hyper parameter.

  • 1) batch_size: 16,z_dim: 128,learning_rate_list: 2e-05,beta1: 0.2,alpha: 0.18 :Discriminator Loss: 0.9422... Generator Loss: 1.1451
  • 2) batch_size: 16,z_dim: 128,learning_rate_list: 2e-05,beta1: 0.4,alpha: 0.12 Epoch 1/1... Discriminator Loss: 0.9679... Generator Loss: 1.0676

Reference:Reference:https://stackoverflow.com/questions/42690721/how-to-interpret-the-discriminators-loss-and-the-generators-loss-in-generative

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.